Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches
نویسندگان
چکیده
There are more than 962 million people aged 60 and up globally. Physical activity declines as get older, does their capacity to undertake everyday tasks, effecting both physical mental health. Many researchers use machine learning deep methods recognize human activities, but very few studies have been focused on recognition of elderly people. This paper focuses providing assistance by monitoring activities in different indoor outdoor environments using gyroscope accelerometer data collected from a smart phone. Smart phones routinely used monitor the persons with impairments; routine such sitting, walking, going upstairs, downstairs, standing, lying included dataset. Conventional Machine Learning Deep algorithms k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, Long Short-Term Memory Network for recognition. is recurrent neural network variation that best suited handling temporal sequences. Two-fold ten-fold cross-validation were performed show effect changing training testing Among all classification techniques, proposed gave accuracy 95.04%. However, 89.07% low computational time 0.42 min 10-fold cross-validation.
منابع مشابه
Comparing Deep and Classical Machine Learning Methods for Human Activity Recognition using Wrist Accelerometer
متن کامل
Named Entity Recognition in Persian Text using Deep Learning
Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...
متن کاملMachine Learning for Activity Recognition
This paper surveys the activity recognition task from a machine learning perspective. I give a definition of this problem, and I classify different activity recognition problems into two categories. I show the activities can be hierarchical, and based on such hierarchies I synthesize a language to describe activities. I give a general criteria set to evaluate activity recognition methods. I sum...
متن کاملLearning Deep Features for kNN-Based Human Activity Recognition
A CBR approach to Human Activity Recognition (HAR) uses the kNN algorithm to classify sensor data into different activity classes. Different feature representation approaches have been proposed for sensor data for the purpose of HAR. These include shallow features, which can either be hand-crafted from the time and frequency domains, or the coefficients of frequency transformations. Alternative...
متن کاملFacial Expression Recognition in Older Adults using Deep Machine Learning
Facial Expression Recognition is still one of the challenging fields in pattern recognition and machine learning science. Despite efforts made in developing various methods for this topic, existing approaches lack generalizability and almost all studies focus on more traditional hand-crafted features extraction to characterize facial expressions. Moreover, effective classifiers to model the spa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information
سال: 2022
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info13060275